cmaes 0.1.0

Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation for Python 3.

CMA-ES

Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES)[1] implementation.

Himmelblau function.

Rosenbrock function.

Quadratic function.

These GIF animations are generated by visualizer.py.

Installation

Supported Python versions are 3.6 or later.

$ pip install cmaes

Usage

This library provides two interfaces that an Optuna's sampler interface and a low-level interface. I strongly recommend you to use this library via Optuna.

Optuna's sampler interface

Optuna [2] is an automatic hyperparameter optimization framework. Optuna officially implements a sampler based on pycma. It achieves almost the same performance. But this library is faster and simple.

import optuna
from cmaes.sampler import CMASampler

def objective(trial: optuna.Trial):
    x1 = trial.suggest_uniform("x1", -4, 4)
    x2 = trial.suggest_uniform("x2", -4, 4)
    return (x1 - 3) ** 2 + (10 * (x2 + 2)) ** 2

def main():
    sampler = CMASampler()
    study = optuna.create_study(sampler=sampler)
    study.optimize(objective, n_trials=250)

if __name__ == "__main__":
    main()

Note that CMASampler doesn't support categorical distributions. Although pycma's sampler supports categorical distributions, it also has a problem (especially on high-cardinality categorical distribution). If your search space contains a categorical distribution, please use TPESampler.

Low-level interface

import numpy as np
from cmaes.cma import CMA

def quadratic(x1: float, x2: float):
    return (x1 - 3) ** 2 + (10 * (x2 + 2)) ** 2

def main():
    cma_es = CMA(mean=np.zeros(2), sigma=1.3)

    best_value = float("inf")
    best_param = None

    for generation in range(50):
        solutions = []
        for _ in range(cma_es.population_size):
            z, x = cma_es.ask()
            evaluation = quadratic(x[0], x[1])

            if evaluation < best_value:
                best_value = evaluation
                best_param = x

            solutions.append((z, evaluation))

        cma_es.tell(solutions)
        print(f"#{generation}: {best_value} (x1={best_param[0]}, x2 = {best_param[1]})")

    print(f"RESULT: {best_value} (x1={best_param[0]}, x2 = {best_param[1]})")

if __name__ == "__main__":
    main()

Benchmark results

Rosenbrock function	Six-Hemp Camel function

This implementation (green) stands comparison with pycma (blue). See benchmark for details.

Links

Other libraries:

I respect all libraries involved in CMA-ES.

• pycma : Most famous CMA-ES implementation by Nikolaus Hansen.
• cma-es : A Tensorflow v2 implementation.

References:

• [1] N. Hansen, The CMA Evolution Strategy: A Tutorial. arXiv:1604.00772, 2016.
• [2] Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19), August 4–8, 2019.